We generate and store triplets of (scan-image \(S_h\) , map of the environment \(M_h\) , ground truth likelihood \(\tilde{p}^{(0)}(z_t|x)\) ) while randomly moving the robot in the simulator and randomly resetting the initial pose after every \(T\)  time steps and randomly resetting the environment after every \(e\)  such episodes. We train it on \(m\)  such environments (maps).
Training on these triplets using Resnet-152 [1]} or Densenet-121 [2]} gave very good results in simulation but did not transfer when these models are transferred to real robot in a real environment. This demonstrates the need for domain randomization to achieve zero-shot transfer. See REF  for further details.
